copula-models

Weighted residual empirical processes in semi-parametric copula adjusted for regression

Yue Zhao, Irène Gijbels and Ingrid Van Keilegom / 2022-12-07

In this post we first review the concept of semi-parametric copula and the accompanying estimation procedure of pseudo-likelihood estimation (PLE). We then generalize the estimation problem to the setting where the copula signal is hidden in a semi- or non-parametric regression model. Under this…

causal-inference

Heterogeneous Treatment Effects with Instrumental Variables: A Causal Machine Learning Approach

Falco J. Bargagli Stoffi / 2021-12-06

In our forthcoming paper on Annals of Applied Statistics, we propose a new method – which we call Bayesian Causal Forest with Instrumental Variable (BCF-IV) – to interpretably discover the subgroups with the largest or smallest causal effects in an instrumental variable setting. These are many…

generalized-linear-models

A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models

Haim Bar, James Booth and Martin T. Wells / 2021-03-13

In the toolbox of most scientists over the past century, there have been few methods as powerful and as versatile as linear regression. The introduction of the generalized linear model (GLM) framework in the 1970’s extended the inferential and predictive capabilities to binary or count data. While…

compositional-data

Compositional scalar-on-function regression as a tool (not only) for geological data

Ivana Pavlů, Karel Hron / 2021-03-10

Compositional data are characterized by the fact that the relevant information is contained not necessarily in the absolute values but rather in the relative proportions between particular components. As an example, take household expenditures for different purposes (housing, groceries, travel etc.)…